Preterm birth is the leading cause of death in young children, and is often associated with long-term neurodevelopmental impairment in children that reach school age. Parenteral and enteral nutrition play a crucial role in the development of the infant admitted to the neonatal intensive care unit (NICU), as it is the only mean of nutrition for the neonates. However, standard nutrition protocols, not individualized to the neonate's specific needs, are followed. A personalization of parenteral and enteral nutrition could help in the development of the infant brain, and be associated to better clinical outcomes. In particular, hypoglycaemic and hyperglycaemic episodes have to be avoided, as they are associated with cognitive impairments and increased mortality rates. Therefore, the availability of a nutritional clinical advisor, able to suggest the optimal amount of glucose to feed the hospitalized neonates, would be of great help in the NICU. Needless to say, the preterm neonates are very fragile and glycaemic control algorithms have to be carefully tested before being applied in this population. Therefore, the first step toward the design of these tools, is to build up a model able to simulate reliable glucose traces of neonates, to safely and effectively test such advisory systems. The aim of this study is thus to build a Neonate Glucose Simulator, able to generate reliable glucose time courses and usable to optimize the control strategy and avoid hyperglycaemic and hypoglycaemic episodes. The simulator must include a model of glucose-insulin-C-peptide interaction, possibly accounting for the metabolic processes known to be altered in infants born preterm, a model of both parenteral and enteral nutrition and a set of virtual neonates representative of real ones. The proposed Neonate Glucose Simulator was equipped with a population of 100 virtual neonates and the glucose time courses obtained for the virtual subjects were compared with real data collected on a population of infants born preterm, monitored during their stay in the NICU, demonstrating the reliability of the newly built tool.

The Neonate Glucose Simulator: A New Tool for Testing a Nutritional Clinical Advisor to Regulate Glycemia in Preterm Infants admitted to the Neonatal Intensive Care Unit

Marchiori, Hadija;Bonet, Jacopo;Galderisi, Alfonso;Man, Chiara Dalla
2025

Abstract

Preterm birth is the leading cause of death in young children, and is often associated with long-term neurodevelopmental impairment in children that reach school age. Parenteral and enteral nutrition play a crucial role in the development of the infant admitted to the neonatal intensive care unit (NICU), as it is the only mean of nutrition for the neonates. However, standard nutrition protocols, not individualized to the neonate's specific needs, are followed. A personalization of parenteral and enteral nutrition could help in the development of the infant brain, and be associated to better clinical outcomes. In particular, hypoglycaemic and hyperglycaemic episodes have to be avoided, as they are associated with cognitive impairments and increased mortality rates. Therefore, the availability of a nutritional clinical advisor, able to suggest the optimal amount of glucose to feed the hospitalized neonates, would be of great help in the NICU. Needless to say, the preterm neonates are very fragile and glycaemic control algorithms have to be carefully tested before being applied in this population. Therefore, the first step toward the design of these tools, is to build up a model able to simulate reliable glucose traces of neonates, to safely and effectively test such advisory systems. The aim of this study is thus to build a Neonate Glucose Simulator, able to generate reliable glucose time courses and usable to optimize the control strategy and avoid hyperglycaemic and hypoglycaemic episodes. The simulator must include a model of glucose-insulin-C-peptide interaction, possibly accounting for the metabolic processes known to be altered in infants born preterm, a model of both parenteral and enteral nutrition and a set of virtual neonates representative of real ones. The proposed Neonate Glucose Simulator was equipped with a population of 100 virtual neonates and the glucose time courses obtained for the virtual subjects were compared with real data collected on a population of infants born preterm, monitored during their stay in the NICU, demonstrating the reliability of the newly built tool.
2025
IFAC-PapersOnLine
1st IFAC Workshop on Engineering Diabetes Technologies, EDT 2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3563239
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